import argparse
import os
import time
from pathlib import Path
import cv2
import torch
import torch.backends.cudnn as cudnn
from playsound import playsound
from threading import Thread
from models.experimental import attempt_load
from utils.datasets import LoadStreams, LoadImages
from utils.general import check_img_size, check_requirements, check_imshow, non_max_suppression, apply_classifier, \
    scale_coords, xyxy2xywh, strip_optimizer, set_logging, increment_path, save_one_box
from utils.plots import colors, plot_one_box
from utils.torch_utils import select_device, load_classifier, time_synchronized
without_mask = './load_file/without_mask.mp3'
mask_weared_incorrect = './load_file/mask_weared_incorrect.mp3'
with_mask = './load_file/with_mask.mp3'
def Play(flag=-1, nc=3):
    if nc == 3:
        if flag == 0:
            playsound(without_mask)
        elif flag == 1:
            playsound(mask_weared_incorrect)
        elif flag == 2:
            playsound(with_mask)
    elif nc == 2:
        if flag == 0:
            playsound(without_mask)
        elif flag == 1:
            playsound(with_mask)
@torch.no_grad()
def detect(opt):
    global play_label_flag
    play_label_flag = -1
    weights, view_img, save_txt, imgsz, play_sound = opt.weights, opt.view_img, opt.save_txt, opt.img_size, opt.play_sound
    for source_ele in opt.list:
        source = source_ele
        save_img = not opt.nosave and not source.endswith('.txt')
        webcam = source.isnumeric() or source.endswith('.txt') or source.lower().startswith(
            ('rtsp://', 'rtmp://', 'http://', 'https://'))
        save_dir = increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok)
        (save_dir / 'labels' if save_txt else save_dir).mkdir(parents=True, exist_ok=True)
        set_logging()
        device = select_device(opt.device)
        half = device.type != 'cpu'
        model = attempt_load(weights, map_location=device)
        stride = int(model.stride.max())
        imgsz = check_img_size(imgsz, s=stride)
        names = model.module.names if hasattr(model, 'module') else model.names
        if half:
            model.half()
        classify = False
        if classify:
            modelc = load_classifier(name='resnet101', n=2)
            modelc.load_state_dict(torch.load('weights/resnet101.pt', map_location=device)['model']).to(device).eval()
        vid_path, vid_writer = None, None
        if webcam:
            view_img = check_imshow()
            cudnn.benchmark = True
            dataset = LoadStreams(source, img_size=imgsz, stride=stride)
        else:
            dataset = LoadImages(source, img_size=imgsz, stride=stride)
        if device.type != 'cpu':
            model(torch.zeros(1, 3, imgsz, imgsz).to(device).type_as(next(model.parameters())))
        t0 = time.time()
        file_name = 0
        for path, img, im0s, vid_cap in dataset:
            img = torch.from_numpy(img).to(device)
            img = img.half() if half else img.float()
            img /= 255.0
            if img.ndimension() == 3:
                img = img.unsqueeze(0)
            t1 = time_synchronized()
            pred = model(img, augment=opt.augment)[0]
            pred = non_max_suppression(pred, opt.conf_thres, opt.iou_thres, opt.classes, opt.agnostic_nms,
                                       max_det=opt.max_det)
            t2 = time_synchronized()
            if classify:
                pred = apply_classifier(pred, modelc, img, im0s)
            for i, det in enumerate(pred):
                if webcam:
                    p, s, im0, frame = path[i], f'{i}: ', im0s[i].copy(), dataset.count
                else:
                    p, s, im0, frame = path, '', im0s.copy(), getattr(dataset, 'frame', 0)
                p = Path(p)
                save_path = str(save_dir / p.name)
                txt_path = str(save_dir / 'labels' / p.stem) + (
                    '' if dataset.mode == 'image' else f'_{frame}')
                s += '%gx%g ' % img.shape[2:]
                gn = torch.tensor(im0.shape)[[1, 0, 1, 0]]
                imc = im0.copy() if opt.save_crop else im0
                if len(det):
                    det[:, :4] = scale_coords(img.shape[2:], det[:, :4], im0.shape).round()
                    for c in det[:, -1].unique():
                        n = (det[:, -1] == c).sum()
                        s += f"{n} {names[int(c)]}{'s' * (n > 1)}, "
                    object_nums = 0
                    for *xyxy, conf, cls in reversed(det):
                        object_nums += 1
                        if save_txt:
                            xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist()
                            line = (cls, *xywh, conf) if opt.save_conf else (cls, *xywh)
                            with open(txt_path + '.txt', 'a') as f:
                                f.write(('%g ' * len(line)).rstrip() % line + '\n')
                        if save_img or opt.save_crop or view_img:
                            c = int(cls)
                            label = None if opt.hide_labels else (
                                names[c] if opt.hide_conf else f'{names[c]} {conf:.2f}')
                            if play_sound == 'open':
                                if object_nums == 1:
                                    if play_label_flag == -1:
                                        if label.split(' ')[0] == 'without_mask':
                                            play_label_flag = 0
                                            Thread(target=Play, args=(0, 3,)).start()
                                        elif label.split(' ')[0] == 'mask_weared_incorrect':
                                            play_label_flag = 1
                                            Thread(target=Play, args=(1, 3,)).start()
                                        elif label.split(' ')[0] == 'with_mask':
                                            play_label_flag = 2
                                            Thread(target=Play, args=(2, 3,)).start()
                                    elif play_label_flag == 0:
                                        if label.split(' ')[0] == 'mask_weared_incorrect':
                                            play_label_flag = 1
                                            Thread(target=Play, args=(1, 3,)).start()
                                        elif label.split(' ')[0] == 'with_mask':
                                            play_label_flag = 2
                                            Thread(target=Play, args=(2, 3,)).start()
                                    elif play_label_flag == 1:
                                        if label.split(' ')[0] == 'without_mask':
                                            play_label_flag = 0
                                            Thread(target=Play, args=(0, 3,)).start()
                                        elif label.split(' ')[0] == 'with_mask':
                                            play_label_flag = 2
                                            Thread(target=Play, args=(2, 3,)).start()
                                    elif play_label_flag == 2:
                                        if label.split(' ')[0] == 'without_mask':
                                            play_label_flag = 0
                                            Thread(target=Play, args=(0, 3,)).start()
                                        elif label.split(' ')[0] == 'mask_weared_incorrect':
                                            play_label_flag = 1
                                            Thread(target=Play, args=(1, 3,)).start()
                            plot_one_box(xyxy, im0, label=label, color=colors(c, True),
                                         line_thickness=opt.line_thickness)
                            if opt.save_crop:
                                save_one_box(xyxy, imc, file=save_dir / 'crops' / names[c] / f'{p.stem}.jpg', BGR=True)
                else:
                    play_label_flag = -1
                print(f'{s}Done. ({t2 - t1:.3f}s)')
                if view_img:
                    file_path = './temp_images/' + str(file_name).zfill(8) + '.jpg'
                    if not os.path.exists('./temp_images/'):
                        os.mkdir('./temp_images/')
                    Thread(target=cv2.imwrite, args=(file_path, im0,)).start()
                    file_name += 1
                    try:
                        if file_name >= 8:
                            if os.path.exists('./temp_images/' + str(file_name - 8).zfill(8) + '.jpg'):
                                Thread(target=os.remove, args=('./temp_images/' + str(file_name - 8).zfill(8) + '.jpg',)).start()
                    except Exception:
                        pass
                    if opt.display_result == 'open':
                        cv2.imshow(str(p), im0)
                        cv2.waitKey(1)
                if save_img:
                    if dataset.mode == 'image':
                        cv2.imwrite(save_path, im0)
                    else:
                        if vid_path != save_path:
                            vid_path = save_path
                            if isinstance(vid_writer, cv2.VideoWriter):
                                vid_writer.release()
                            if vid_cap:
                                fps = vid_cap.get(cv2.CAP_PROP_FPS)
                                w = int(vid_cap.get(cv2.CAP_PROP_FRAME_WIDTH))
                                h = int(vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
                            else:
                                fps, w, h = 30, im0.shape[1], im0.shape[0]
                                save_path += '.mp4'
                            vid_writer = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*'mp4v'), fps, (w, h))
                        vid_writer.write(im0)
        if save_txt or save_img:
            s = f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}" if save_txt else ''
            print(f"Results saved to {save_dir}{s}")
        print(f'Done. ({time.time() - t0:.3f}s)')
if __name__ == '__main__':
    parser = argparse.ArgumentParser()
    parser.add_argument('--weights', '-w', nargs='+', type=str, default='./weights/2classes.pt',
                        help='model.pt path(s)')
    parser.add_argument('--source', '-s', dest="list", nargs='+', type=str, default='0',
                        help='source')
    parser.add_argument('--display-result', '-dr', type=str, default='close',
                        help='display detect result video, open 打开, close 关闭')
    parser.add_argument('--play-sound', '-ps', type=str, default='open',
                        help='play with_mask or without_mask sound etc., open 打开, close 关闭')
    parser.add_argument('--img-size', type=int, default=640, help='inference size (pixels)')
    parser.add_argument('--conf-thres', type=float, default=0.25, help='object confidence threshold')
    parser.add_argument('--iou-thres', type=float, default=0.45, help='IOU threshold for NMS')
    parser.add_argument('--max-det', type=int, default=1000, help='maximum number of detections per image')
    parser.add_argument('--device', '-d', default='0', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
    parser.add_argument('--view-img', action='store_true', help='display results')
    parser.add_argument('--save-txt', action='store_true', help='save results to *.txt')
    parser.add_argument('--save-conf', action='store_true', help='save confidences in --save-txt labels')
    parser.add_argument('--save-crop', action='store_true', help='save cropped prediction boxes')
    parser.add_argument('--nosave', action='store_true', help='do not save images/videos')
    parser.add_argument('--classes', nargs='+', type=int, help='filter by class: --class 0, or --class 0 2 3')
    parser.add_argument('--agnostic-nms', action='store_true', help='class-agnostic NMS')
    parser.add_argument('--augment', action='store_true', help='augmented inference')
    parser.add_argument('--update', action='store_true', help='update all models')
    parser.add_argument('--project', default='runs/detect', help='save results to project/name')
    parser.add_argument('--name', default='exp', help='save results to project/name')
    parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment')
    parser.add_argument('--line-thickness', default=3, type=int, help='bounding box thickness (pixels)')
    parser.add_argument('--hide-labels', default=False, action='store_true', help='hide labels')
    parser.add_argument('--hide-conf', default=False, action='store_true', help='hide confidences')
    opt = parser.parse_args()
    print(opt)
    check_requirements(exclude=('tensorboard', 'pycocotools', 'thop'))
    if opt.update:
        for opt.weights in ['yolov3.pt', 'yolov3-spp.pt', 'yolov3-tiny.pt']:
            detect(opt=opt)
            strip_optimizer(opt.weights)
    else:
        detect(opt=opt)